Transformers
Safetensors
English
esmfold2
biology
esm
protein
protein-structure-prediction
structure-prediction
protein-design
3d-structure
confidence-estimation
molecular-dynamics
custom_code
Instructions to use biohub/ESMFold2-Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMFold2-Fast with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("biohub/ESMFold2-Fast", trust_remote_code=True) model = AutoModel.from_pretrained("biohub/ESMFold2-Fast", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 327 Bytes
918beed 93a69ea 918beed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"backend": "tokenizers",
"bos_token": "<cls>",
"chain_break_token": "|",
"cls_token": "<cls>",
"eos_token": "<eos>",
"extra_special_tokens": {},
"mask_token": "<mask>",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<pad>",
"tokenizer_class": "EsmcTokenizer",
"unk_token": "<unk>"
}
|